用深度高斯过程预测电池容量，提升寿命评估精度
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
- 结合LSTM与半参数高斯过程建模充放电过程数据
- 在NASA数据集上测试均方误差低至1.2%
- 适合电池健康管理与智能储能系统研发者
锂离子电池在能源供应中日益普及，但其容量随充放电循环次数增加而衰减，威胁储能系统的耐用性。准确预测电池容量是保障系统效率与可靠性的关键，而每次循环中的电池状态信息尚未被充分挖掘。本文提出一种基于半参数深度高斯过程的回归框架SDG-L，通过引入LSTM特征提取器，更好地利用充放电过程中的辅助状态信息。基于NASA数据集的实验表明，所提方法在测试集上平均均方误差（MSE）为1.2%。相比现有方法，SDG-L表现更优，并通过消融实验验证了框架的有效性。
原文摘要 · Abstract (English)
Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging cycles. An accurate capacity prediction is the key to ensure system efficiency and reliability, where the exploitation of battery state information in each cycle has been largely undervalued. In this paper, we propose a semiparametric deep Gaussian process regression framework named SDG-L to give predictions based on the modeling of time series battery state data. By introducing an LSTM feature extractor, the SDG-L is specially designed to better utilize the auxiliary profiling information during charging/discharging process. In experimental studies based on NASA dataset, our proposed method obtains an average test MSE error of 1.2%. We also show that SDG-L achieves better performance compared to existing works and validate the framework using ablation studies.
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